Search (10 results, page 1 of 1)

Cai, F.; Rijke, M. de: Learning from homologous queries and semantically related terms for query auto completion (2016) 0.02

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Content: Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457315001454.
Source: Information processing and management. 52(2016) no.4, S.628-643

Meij, E.; Trieschnigg, D.; Rijke, M. de; Kraaij, W.: Conceptual language models for domain-specific retrieval (2010) 0.01

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Source: Information processing and management. 46(2010) no.4, S.448-469

Kenter, T.; Balog, K.; Rijke, M. de: Evaluating document filtering systems over time (2015) 0.01

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Source: Information processing and management. 51(2015) no.6, S.791-808

Hofmann, K.; Balog, K.; Bogers, T.; Rijke, M. de: Contextual factors for finding similar experts (2010) 0.00
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Abstract

Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-centered perspective. The main focus has been on developing content-based algorithms similar to document search. These algorithms identify matching experts primarily on the basis of the textual content of documents with which experts are associated. Other factors, such as the ones identified by expertise-seeking models, are rarely taken into account. In this article, we extend content-based expert-finding approaches with contextual factors that have been found to influence human expert finding. We focus on a task of science communicators in a knowledge-intensive environment, the task of finding similar experts, given an example expert. Our approach combines expertise-seeking and retrieval research. First, we conduct a user study to identify contextual factors that may play a role in the studied task and environment. Then, we design expert retrieval models to capture these factors. We combine these with content-based retrieval models and evaluate them in a retrieval experiment. Our main finding is that while content-based features are the most important, human participants also take contextual factors into account, such as media experience and organizational structure. We develop two principled ways of modeling the identified factors and integrate them with content-based retrieval models. Our experiments show that models combining content-based and contextual factors can significantly outperform existing content-based models.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.994-1014

Tsagkias, M.; Larson, M.; Rijke, M. de: Predicting podcast preference : an analysis framework and its application (2010) 0.00

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Source: Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.374-391

Huurnink, B.; Hollink, L.; Heuvel, W. van den; Rijke, M. de: Search behavior of media professionals at an audiovisual archive : a transaction log analysis (2010) 0.00

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Source: Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1180-1197

He, J.; Meij, E.; Rijke, M. de: Result diversification based on query-specific cluster ranking (2011) 0.00

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Source: Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.550-571

Berendsen, R.; Rijke, M. de; Balog, K.; Bogers, T.; Bosch, A. van den: On the assessment of expertise profiles (2013) 0.00

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Source: Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2024-2044

Bron, M.; Gorp, J. Van; Rijke, M. de: Media studies research in the data-driven age : how research questions evolve (2016) 0.00

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Source: Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1535-1554

Graus, D.; Odijk, D.; Rijke, M. de: ¬The birth of collective memories : analyzing emerging entities in text streams (2018) 0.00

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Source: Journal of the Association for Information Science and Technology. 69(2018) no.6, S.773-786

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